Below you will find pages that utilize the taxonomy term “network inference”
Blog
Last Submissions to the Challenge
Today, I submitted in silico and experimental data network inference results on Synapse for the next leaderboard on this Wednesday.
For experimental part, I had to exclude edges with FGFR1 and FGFR3 because the data lacks phosphorylated forms of these proteins and networks must be constructed using only phosphoproteins in the data.
Since there was an update for in silico part, I had to modify the script and resubmit the results.
Blog
Network Visualization Using Cytoscape
Cytoscape is a nice tool to visualize network for better understanding and delivery. I used it for in silico data network visualization and the result was really pretty. Now, I have networks constructed using experimental data from HPN-DREAM Challenge.
In this post, I want to demonstrate how to visualize a network with scores. I’m using Cytoscape 2.8 on Ubuntu 12.
First, the network will be read from a SIF file which is default format of Cytoscape for networks.
Blog
Plotting Expression Curves for Experimental Data
As I can plot expression curves for in silico data. I moved on experimental data which is more complex and larger. This data is the result of RPPA experiments on different breast cancer cell lines and it includes protein abundance measurements for about 45 phophoproteins. These phosphoproteins are treated with different inhibitors and stimuli and by comparing their expressions, I will try to infer relations between them.
Before moving on inferring part, I want to have a script that can plot the graphs so that I can see particular results for specific cases.
Blog
Experimental Data Optimization for Network Inference
As I mentioned in my previous post, experimental data from the challenge has missing data values that create problems during analyses. To solve it, first thing I did was to optimize data, which includes detecting missing conditions and putting NAs for data values and sorting them if necessary.
I wrote two functions in the script. First one ranks the data according to the fashion and sorts it based on these ranks.
Blog
Working with Experimental Data from Network Inference Challenge
As I almost finished with in silico data, I moved on to analyses of experimental data using the same script. But since the characteristics of data is somehow different, before inferring network, I need to modify the script to be able to read experimental data files.
These differences include missing data values for some conditions. This makes analyses difficult because I have to estimate a value for them and this will decrease the confidence score of edges.
Blog
In silico Network Inference Last Improvements and Visualization of Result in Cytoscape
I’m almost done with the analysis of in silico data, although I need to decide if I need further analysis with the inhibiting parent nodes in the network. Last, I couldn’t filter out duplicate edges, which were scored differently. Now, with some improvements in the script, low scores duplicates are filtered and there is a better final list of edges which is ready to be visualized.
I also tried visualizing it on Cytoscape.
Blog
Latest Progress on Network Inference and Edge Scoring
I have improved network inference part of the script slightly by changing the way of comparing intervention (presence of inhibitor and stimulus) and no intervention (presence of stimulus) data from in silico part.
Now, I’m using a function (simp) from an R package called StreamMetabolism, which gets time points and data values and (does integration) calculates the area under the curve (Sefick, 2009). I do this integration for both condition and then I compare them.
Blog
Scoring Edges Network Inference HPN-DREAM Challenge
Yesterday, I managed to infer a network for some part of in silico data from the challenge. Since the challenge also asks for scoring the edges in networks, I developed the script further and add a function for that.
edgeScorer function gets data object of average time points for each curve in intervention/no-intervention sets and scores each edge for each set of conditions. For this, first, it looks for the largest difference among the sets and set it as maxDifference and later, it stores differences divided by maxDifference in another data object.
Blog
Determining Edges More Progress on Network Inference
Lately, I have been writing an R script to infer network using in silico data. Last version of the script was reading MIDAS file and plotting expression profiles. I have modified it and now it reads MIDAS file, does some analyses and prints causal relations to a file. This file is a SIF file as required.
This dataset is generated with 20 antibodies but only 3 of them are perturbed. Also, for one, stimulus is missing.
Blog
Plotting Expression Profiles Data Analysis for Network Inference
For in silico data network inference I decided to develop a script because the existing tools have bugs and they are not compatible with the data. At the same time, I will try to report bugs and the compatibility issues to developers.
in silico data has 660 experiment results of 20 antibodies, 4 kinds of stimuli and 3 kinds of inhibitors. Antibodies are treated with a stimulus, say at t_0 and in the case of inhibitors, say at t_i, antibodies are pre-incubated for some time (t_pre) and then, treated with a stimulus.
Blog
Webinar on HPN-DREAM Breast Cancer Network Inference Challenge
DREAM8 organizers plan a webinar about HPN-DREAM Breast Cancer Network Inference Challenge on July 19, at 10:30 - 11:30 (PDT / UTC -7). General setup of the challenge, demo submissions to the leaderboard will be discussed and also questions about the challenge will be accepted during webinar. The number of the participants to the challenge is also announced: 138.
Registration to the webinar is done using this form. There are limited number of “seats”, but later recordings will be published.
Blog
Network Inference Challenge in silico Data
I had a meeting with BiGCaT this week and we discussed DREAM Breast Cancer Challenge. I presented the challenge and also some ways that I have found to solve the first sub-challenge network inference. Tina, from BiGCaT, suggested starting with in silico data which is much simpler than breast cancer data. Later, I can use the methods I develop for in silico data in experimental data.
in silico data contains 20 antibodies, 3 inhibitors and 2 ligand stimuli with 2 different concentration for each.
Blog
Playing around with CellNOptR Tool and MIDAS File
With CellNOptR, we will try to construct network models for the challenge. For this, the tool needs two inputs. First one is a special data object called CNOlist that stores vectors and matrices of data. Second one is a .SIF file that contains prior knowledge network which can be obtained from pathway database and analysis tools.
CNOlist contains following fields: namesSignals, namesCues, namesStimuli and namesInhibitors, which are vectors storing the names of measurements.
Blog
Progress on Network Inference Sub-Challenge
This sub-challenge has several requirements:
Directed and causal edges on the models (32 models - 4 cell lines × 8 stimuli) Edges should be scored (normalizing to range between 0 and 1) that will show confidence Nodes will be phosphoproteins from the data Prior knowledge network (that can be constructed using pathway databases) is allowed to be used (actually this is a must for some network inference tools) First thing was to look for existing tools.
Blog
Network Inference DREAM Breast Cancer Challenge
The inference of causal edges are described as the change on a node seen after the intervention of another node. If the curves obtained over time overlap (under intervention or no intervention), then there is no relation. Otherwise, we can draw an edge between those nodes and according to the level, up or down, the edge will be activating or inhibiting. These causal edges are context-specific so in different cell line data, we may have different relations.
Blog
DREAM Breast Cancer Sub-challenges
I have been going over the sub-challenges before attempting to solve them. As I mentioned, there are three sub-challenges and somehow they are connected.
First, using given data and other possible data sources such as pathway databases, the causal signaling network of the phosphoproteins. There are 4 cell lines and 8 stimulus so they make total 32 networks at the end. Nodes are phosphoproteins and edges should be directed and causal (activator or inhibitor).
Blog
HPN-DREAM Breast Cancer Network Inference Challenge
Understanding signaling networks might bring more insights on cancer treatment because cells respond to their environment by activating these networks and phosphorylation reactions play important roles in these networks.
The goal of this challenge is to advance our ability and knowledge on signaling networks inference and protein phosphorylation dynamics prediction. Also, we are asked to develop a visualization method for the data.
The dataset provided is extensive and a result of RPPA (reverse-phase protein array) experiments.
Blog
Dream Challenge
This year, 8th Dream Challenge takes place and I will be working on this project as my internship job in BiGCaT, Bioinformatics, UM. The challenge brings scientists to catalyze the interaction between experiment and theory in the area of cellular network inference and quantitative model building in systems biology (as said on their webpage).
In this competition, I will work on a specific challenge about network modeling, dynamic response predictions and data visualization.